
'''simple docstring'''

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 						_UpperCAmelCase					(	_lowerCamelCase    :							int	,   _lowerCamelCase    :							str=False		)     ->    Union[str, Any]:
  _lowerCAmelCase							:	Union[str, Any] 					=       OmegaConf.load(snake_case__		)
  if display:
    print(yaml.dump(OmegaConf.to_container(snake_case__		)		)		)
  return config


def 						_UpperCAmelCase					(	_lowerCamelCase    :							int	,   _lowerCamelCase    :							Optional[int]=None	,   _lowerCamelCase    :							Any=None		)     ->    Tuple:
  if conf_path is None:
    _lowerCAmelCase							:	List[str] 					=       """./model_checkpoints/vqgan_only.yaml"""
  _lowerCAmelCase							:	Any 					=       load_config(snake_case__	,   display=snake_case__		)
  _lowerCAmelCase							:	Any 					=       VQModel(**config.model.params		)
  if ckpt_path is None:
    _lowerCAmelCase							:	List[str] 					=       """./model_checkpoints/vqgan_only.pt"""
  _lowerCAmelCase							:	str 					=       torch.load(snake_case__	,   map_location=snake_case__		)
  if ".ckpt" in ckpt_path:
    _lowerCAmelCase							:	Optional[Any] 					=       sd["""state_dict"""]
  model.load_state_dict(snake_case__	,   strict=snake_case__		)
  model.to(snake_case__		)
  del sd
  return model


def 						_UpperCAmelCase					(	_lowerCamelCase    :							Dict	,   _lowerCamelCase    :							Optional[int]		)     ->    Dict:
  _lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase							:	str 					=       model.encode(snake_case__		)
  print(f'VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'		)
  _lowerCAmelCase							:	Tuple 					=       model.decode(snake_case__		)
  return xrec


def 						_UpperCAmelCase					(	_lowerCamelCase    :							List[str]	,   _lowerCamelCase    :							Optional[int]=False		)     ->    str:
  _lowerCAmelCase					,       _lowerCAmelCase							:	Union[str, Any] 					=       string.rsplit("""."""	,   1		)
  if reload:
    _lowerCAmelCase							:	str 					=       importlib.import_module(snake_case__		)
    importlib.reload(snake_case__		)
  return getattr(importlib.import_module(snake_case__	,   package=snake_case__		)	,   cls		)


def 						_UpperCAmelCase					(	_lowerCamelCase    :							Optional[int]		)     ->    List[Any]:
  if "target" not in config:
    raise KeyError("""Expected key `target` to instantiate."""		)
  return get_obj_from_str(config["""target"""]		)(**config.get("""params"""	,   {}		)		)


def 						_UpperCAmelCase					(	_lowerCamelCase    :							Dict	,   _lowerCamelCase    :							Optional[Any]	,   _lowerCamelCase    :							Optional[Any]=True	,   _lowerCamelCase    :							Union[str, Any]=True		)     ->    Union[str, Any]:
  _lowerCAmelCase							:	List[Any] 					=       instantiate_from_config(snake_case__		)
  if sd is not None:
    model.load_state_dict(snake_case__		)
  if gpu:
    model.cuda()
  if eval_mode:
    model.eval()
  return {"model": model}



def 						_UpperCAmelCase					(	_lowerCamelCase    :							int	,   _lowerCamelCase    :							str	,   _lowerCamelCase    :							int	,   _lowerCamelCase    :							Tuple		)     ->    Any:
  # load the specified checkpoint
  if ckpt:
    _lowerCAmelCase							:	Dict 					=       torch.load(snake_case__	,   map_location="""cpu"""		)
    _lowerCAmelCase							:	List[str] 					=       pl_sd["""global_step"""]
    print(f'loaded model from global step {global_step}.'		)
  else:
    _lowerCAmelCase							:	List[str] 					=       {"""state_dict""": None}
    _lowerCAmelCase							:	int 					=       None
  _lowerCAmelCase							:	Any 					=       load_model_from_config(config.model	,   pl_sd["""state_dict"""]	,   gpu=snake_case__	,   eval_mode=snake_case__		)["""model"""]
  return model, global_step








"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



